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走向标准化的物联网本体,使用基于机器学习的框架进行无数据交换
Simmi Chawla1, Parul Tomar2, Sapna Gambhir3
1Department of Computer Engineering, J. C. Bose University of Science & Technology, YMCA Faridabad; 18001902005@jcboseust.ac.in.
Journal of visualized experiments : JoVE
|October 27, 2025
概括
本研究介绍了一种用于统一物联网 (IoT) 集成的机器学习框架,通过智能实体结构对齐,增强实时数据交换和跨多种设备的互操作性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 多样化的物联网 (IoT) 设备在实时互操作性和数据交换方面存在挑战,原因是数据模型和协议的多样性.
- 现有的物联网生态系统通常是分散的,阻碍了无集成和通信.
研究的目的:
- 为标准化和自适应物联网集成提出使用基于机器学习的本体统一框架,以实现基于机器学习的本体统一.
- 在异质物联网环境中提高数据交换的一致性和效率.
主要方法:
- 开发了一个整合实时数据流处理,语义相似性分析和自适应本体学映射的框架.
- 采用机器学习来实现跨多种物联网设备的动态本体统一.
- 在模拟和现实环境 (智能家居,医疗保健) 中测试了框架.
主要成果:
- 实现了97%的本体学对齐准确度.
- 将数据交换延迟降低到20毫秒以下.
- 在不同类型的物联网设备之间保持了超过95%的互操作性.
结论:
- 机器学习与语义建模的整合大大提高了物联网系统的性能,可扩展性和适应性.
- 该框架为实时,智能和自动化的物联网互操作性提供了一个强大的,可扩展的解决方案.
- 成功地解决了语义不一致问题,并支持在没有人工干预的情况下进行动态设备上线.
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